<p>Full-Reference IQA Metrics.</p>

Image Quality Assessment (IQA) plays a critical role in image-based decision-making systems, especially in domains requiring high diagnostic precision. Effective feature information is a prerequisite for the high performance of machine learning methods in parasitic organism detection, and the quality of this feature information is influenced by the quality of the images. However, No-Reference IQA (NR-IQA) models have ignored microscopy-based datasets, particularly those involving parasitic organisms such as Cryptosporidium spp. and Giardia spp., which are vital for public health inspection. In this study, PRIQA (Parasite ResNet-101 IQA), a novel deep learning-based NR-IQA model specifically trained on a small parasite image dataset was presented. Using Mean Opinion Scores (MOS) from twenty human evaluators, nine Deep Convolutional Neural Network (DCNN) architectures were benchmarked and identified ResNet-101 as the most robust feature extractor. The features were mapped to MOS using regression models and compared with ten state-of-the-art NR-IQA algorithms. Experimental results demonstrated that PRIQA consistently outperforms existing methods, indicating its suitability as a practical quality control tool for identifying unreliable or low-quality parasite microscopy images and supporting more consistent downstream detection and diagnostic workflows in automated inspection systems.图像质量评估（Image Quality Assessment, IQA）在基于图像的决策系统中扮演着不可或缺的关键角色，尤其在对诊断精度要求严苛的领域中尤为重要。有效的特征信息是机器学习方法实现高性能寄生虫检测的前提，而该特征信息的质量直接受图像本身质量的影响。然而，无参考图像质量评估（No-Reference IQA, NR-IQA）模型却忽略了基于显微镜成像的数据集，尤其是那些包含隐孢子虫属（Cryptosporidium spp.）、贾第鞭毛虫属（Giardia spp.）这类对公共卫生检测至关重要的寄生虫的数据集。本研究提出了一种基于深度学习的新型无参考图像质量评估模型PRIQA（Parasite ResNet-101 IQA，寄生ResNet-101图像质量评估模型），该模型专门在小型寄生虫图像数据集上完成训练。本研究借助20名人类评估者给出的平均意见得分（Mean Opinion Scores, MOS），对9种深度卷积神经网络（Deep Convolutional Neural Network, DCNN）架构开展基准测试，最终确定ResNet-101为性能最稳健的特征提取器。研究采用回归模型将提取的特征映射至平均意见得分，并与10种当前前沿的无参考图像质量评估算法进行对比。实验结果表明，PRIQA的性能始终优于现有方法，这意味着它可作为一款实用的质量控制工具，用于甄别质量不佳或不可靠的寄生虫显微图像，同时能为自动化检测系统中更稳定的下游检测与诊断工作流提供有力支撑。